1. Field of the Invention
This invention relates to the processing of doppler radar signals, where the radar signals contain both targets and unwanted energy in the form of background noise and clutter. Specifically, the invention provides a time efficient method for processing digitized radar signals in order to determine an adaptive target detection threshold which can then be used to distinguish real targets from background noise and clutter.
2. Background Information
Adaptive target detection thresholds (herein refered to as adaptive thresholds) are an important part of any doppler radar system's signal processor. Adaptive thresholds improve the radar system's ability to identify and distinguish targets from background noise, clutter, and other forms of interference.
After receiving the radar return signal, the radar system down-converts the signal from radio frequency (RF) to intermediate frequency (IF). It then digitizes, filters, and stores the information in memory for additional signal processing. The information is stored in the form of a 2-dimensional data array commonly known as a range-doppler matrix. Each data element, also called a range-doppler cell, in the data array represents the amount of radar energy present at that specific range-doppler filter combination.
The radar system's signal processor must then compare each data element in the range-doppler matrix with an adaptive threshold value to determine if a target exists at that range-doppler frequency combination. The radar system will establish that a target exists if the value of the range-doppler cell exceeds the adaptive threshold value.
The value of the adaptive threshold level is a function of the amplitudes in the range-doppler cells surrounding the specific range-doppler cell for which the process must derive the adaptive threshold. Furthermore, the number of surrounding range-doppler cells (data points) needed to effectively compute an adaptive threshold varies with range-to-target, flight attitude, noise, clutter, and intentional interference should it exist. If the environment of the surveillance area is dynamic, the signal processor must continue to vary, or adapt, the number of data points for each unique environmental region in the range-doppler matrix, thus the term "adaptive threshold". Only then will each threshold level accurately reflect the clutter and noise environment for that particular region.
In theory, each range-doppler cell could represent a unique environmental region and require a different set of data points (parameter set) for computing its corresponding adaptive threshold. In theory, the surveillance area could be perfectly flat, with little or no clutter. In this case, the same number of data points could be used to derive the threshold levels. Typically, the environmental characteristics of the radar surveillance area is somewhere between these two extremes.
A typical adaptive threshold computation is illustrated in Equation (1). ##EQU1## .alpha..sub.kl represents the amplitude of the klth cell surrounding the ijth cell for which the threshold value (B.sub.ij) is being determined. .alpha..sub.gh represents the amplitude of a smaller area immediately surrounding the ijth cell which must be subtracted from the amplitudes of the other cells to prevent the target, if one exists, from self-biasing the average amplitude upon which the threshold is based. .gamma..sub.ij represents a scaling multiplier.
The range-doppler matrix is typically divided, for signal processing purposes, into several equally sized subarrays called parallel processing elements. For example, if the range-doppler matrix contains 64 range range gates and 128 doppler filters (8,192 range-doppler cells), the signal processor might divide the matrix into 16 equally sized processing elements, each being 64 range gates by 8 doppler filters (512 range-doppler cells).
Conventional doppler radar systems typically employ what is known as Single Instruction path, Multiple Data path (SIMD) signal processor architecture. This type of signal processing architecture typically assumes an equal distribution of data over a set of parallel processing elements when executing arithmetic operations on an identical instruction stream. When this assumption is true, the typical SIMD signal processing architecture implementation can effectively and efficiently compute adaptive thresholds for the entire range-doppler matrix. When the assumption is not true, in other words, as the data distribution across the parallel processing elements becomes less and less uniform due to variations in the surveillance area environment, the conventional SIMD processor becomes less and less effective.
As stated above, the range-doppler matrix typically reflects radar signal returns over a large surveillance area containing many environmental variations. In order to optimize target detection performance, the radar system's signal processor must be able to apply as many unique parameter sets as necessary to derive adaptive thresholds which accurately reflect each unique environmental region in the range-doppler matrix. The conventional SIMD processor must process each unique parameter set in sequence. Since each sequential operation increases the overall amount of time required to process the data stored in the range-doppler matrix, the signal processor may not have enough time to derive an adaptive threshold for each unique environmental region. The result with conventional radar processing systems to date has been to minimize the number of parameter sets used in order to save valuable processing time. The "trade-off" is that the system may be forced to apply less than optimal parameter sets; therefore, less than optimal adaptive thresholds. This ultimately degrades target detection performance.
The concept of target detection thresholds is not itself unique as is evident in the following U.S. Patents, the disclosures of which are incorporated herein by reference:
U.S. Pat. No. 4,845,500 issued to Cornett et al;
U.S. Pat. No. 4,713,664 issued to Taylor, Jr.;
U.S. Pat. No. 4,486,756 issued to Peregrim et al; and
U.S. Pat. No. 3,720,942 issued to Wilmot et al.
Cornett et al disclose a radar video detector and target tracker in which an adaptive target detection threshold value is calculated for each target on every scan. The threshold values are computed by taking the radar video signals from a target or clutter and averaging the signals over small areas (cells) which are stored in memory for processing. These cells are elements in a matrix `n` azimuth sectors and `m` range bins in dimension. Stored values in the first and last row of cells are processed to establish the mean value and mean deviation value for each row in the window. The smallest values are subtracted from the averaged signals to establish a new stored amplitude for each cell with reduced background noise. Each element is compared with its neighboring elements and a target detection is indicated in a cell when at least one of the two adjacent elements have positive resulting amplitudes.
The Taylor, Jr. patent relates to an adaptive threshold system which is used to set the alarm threshold level for doppler filters. The system uses data corresponding to at least three antenna azimuth positions. The data is derived from adjacent coherent processing intervals in moving target detector (MTD) radar systems. The adaptive threshold level is governed by combinations of three or more azimuth data values in order to make the threshold level more closely match the residue curve rather than the input clutter from a point clutter source. Compensation of the threshold level determined from the three azimuth data values is provided by signals from the zero doppler filter output. Additional compensation is provided for other system variables, such as changes in the scan rate, radar instability, and conventional constant false alarm rate processing. The threshold system combines the largest of the clutter input values with the compensating signals by use of a log power combiner to provide the combined and compensated threshold level.
Peregrim et al describes a method of reducing angle noise in a missile radar. Energy is transmitted in an arbitrarily chosen frequency pair symmetrically disposed about the tuning frequency of the radome of a radar, and the complex monopulse ratios of the return signals are formed. The sum magnitude and the magnitude of the imaginary part of the complex monopulse ratio, determined for each frequency pair, are subjected to selected thresholds in order to reject erroneous data points. A sum channel threshold and a threshold on the imaginary part of the complex monopulse ratios are utilized. Both of these thresholds vary as a function of the missile-to-target range. In addition, a glint threshold is also utilized. The glint threshold is an adaptive threshold predicated on a desired probability of false alarm.
Wilmot et al relates to a system for automatically processing quantized normal and moving target indicator (MTI) radar video to provide improved clutter rejection and improved detection of moving targets in clutter. The quantized video is applied to a mean level detector. The sensitivity of the mean level detector is controlled as a function of the number of detected target reports being stored in an output buffer unit in order to provide the proper threshold. The output of the mean level detector and the quantized normal video are applied to a video selector circuit for automatic selection of subsequent detection and processing.
Although these patents relate to various methods for processing radar signals and enhancing target detection, they do not describe an efficient process for computing a generalized adaptive target detection threshold, where the process for establishing the threshold is independent of complexity of the noise and clutter environment surrounding each and every target in a given surveillance area.